from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.647	0.0	-1	1	NaN	NaN	0.062	0.000	0.195	0.195	See	See
5	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.926	0.0	-1	5	NaN	1.000	0.009	0.000	1.349	1.350	See	See
10	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.471	0.0	1	100	NaN	0.875	0.214	0.004	0.058	0.058	See	See
15	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.013	0.0	NaN	6.311	0.0	-1	100	NaN	NaN	0.048	0.000	0.266	0.266	See	See
20	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.851	0.0	1	5	NaN	1.000	0.001	0.000	16.186	16.522	See	See
25	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.861	0.0	1	1	NaN	0.975	0.061	0.001	0.192	0.192	See	See
30	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.335	0.0	-1	1	NaN	NaN	0.009	0.000	0.518	0.518	See	See
35	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.334	0.0	-1	5	NaN	1.000	0.001	0.000	6.455	6.543	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.307	0.070	NaN	0.000	0.018	-1	1	0.663	0.687	0.175	0.004	104.317	104.339	See	See
2	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.019	0.057	NaN	0.000	0.002	-1	1	0.663	1.000	0.009	0.000	237.415	237.569	See	See
3	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.304	0.010	NaN	0.000	0.304	-1	1	1.000	NaN	0.047	0.000	6.400	6.400	See	See
4	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.023	0.002	NaN	0.000	0.023	-1	1	1.000	0.742	0.175	0.002	0.129	0.129	See	See
6	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.303	0.010	NaN	0.000	0.018	-1	5	0.757	NaN	0.047	0.000	389.453	389.453	See	See
7	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.914	0.038	NaN	0.000	0.003	-1	5	0.757	0.875	0.213	0.001	13.652	13.652	See	See
8	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.322	0.006	NaN	0.000	0.322	-1	5	1.000	0.000	0.009	0.000	35.529	35.549	See	See
9	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.027	0.003	NaN	0.000	0.027	-1	5	1.000	NaN	0.047	0.000	0.561	0.561	See	See
11	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.946	0.097	NaN	0.000	0.019	1	100	0.882	0.000	0.009	0.000	2165.205	2167.974	See	See
12	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.085	0.007	NaN	0.000	0.002	1	100	0.882	NaN	0.047	0.000	44.388	44.388	See	See
13	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.311	0.010	NaN	0.000	0.311	1	100	1.000	0.742	0.175	0.001	1.772	1.772	See	See
14	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.020	0.000	NaN	0.000	0.020	1	100	1.000	1.000	0.009	0.000	2.370	2.371	See	See
16	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.422	0.065	NaN	0.000	0.018	-1	100	0.882	0.687	0.175	0.003	105.151	105.170	See	See
17	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.891	0.038	NaN	0.000	0.003	-1	100	0.882	1.000	0.009	0.000	322.043	322.168	See	See
18	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.308	0.004	NaN	0.000	0.308	-1	100	1.000	NaN	0.009	0.000	34.147	34.147	See	See
19	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.023	0.002	NaN	0.000	0.023	-1	100	1.000	0.967	0.026	0.000	0.887	0.887	See	See
21	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.276	0.030	NaN	0.000	0.018	1	5	0.757	NaN	0.009	0.000	2001.872	2001.872	See	See
22	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.053	0.005	NaN	0.000	0.002	1	5	0.757	0.974	0.027	0.000	76.060	76.062	See	See
23	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.308	0.007	NaN	0.000	0.308	1	5	1.000	1.000	0.001	0.000	430.037	439.697	See	See
24	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.019	0.000	NaN	0.000	0.019	1	5	1.000	NaN	0.009	0.000	2.127	2.127	See	See
26	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	18.268	0.160	NaN	0.000	0.018	1	1	0.663	1.000	0.001	0.000	23367.079	23839.789	See	See
27	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.185	0.009	NaN	0.001	0.001	1	1	0.663	NaN	0.009	0.000	128.065	128.065	See	See
28	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.308	0.005	NaN	0.000	0.308	1	1	1.000	0.975	0.061	0.000	5.038	5.038	See	See
29	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.020	0.002	NaN	0.000	0.020	1	1	1.000	1.000	0.001	0.000	24.393	24.827	See	See
31	KNeighborsClassifier_brute_force	True	predict	100000	1000	2	3.702	0.015	NaN	0.000	0.004	-1	1	0.896	0.974	0.027	0.000	135.299	135.321	See	See
32	KNeighborsClassifier_brute_force	False	predict	100000	1000	2	1.859	0.026	NaN	0.000	0.002	-1	1	0.896	1.000	0.001	0.000	2582.300	2636.815	See	See
33	KNeighborsClassifier_brute_force	True	predict	100000	1	2	0.240	0.005	NaN	0.000	0.240	-1	1	1.000	NaN	0.009	0.000	26.151	26.151	See	See
34	KNeighborsClassifier_brute_force	False	predict	100000	1	2	0.008	0.003	NaN	0.000	0.008	-1	1	1.000	0.967	0.026	0.000	0.313	0.313	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.581	0.0	NaN	0.022	0.0	-1	1	NaN	NaN	0.857	0.000	4.176	4.176	See	See
5	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.939	0.0	NaN	0.020	0.0	-1	5	NaN	1.000	0.000	0.000	8333.050	9091.946	See	See
10	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.826	0.0	NaN	0.021	0.0	1	100	NaN	0.940	0.619	0.016	6.182	6.184	See	See
15	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.204	0.0	NaN	0.019	0.0	-1	100	NaN	NaN	0.735	0.000	5.718	5.718	See	See
20	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.006	0.0	NaN	0.020	0.0	1	5	NaN	1.000	0.000	0.000	36112.554	48886.243	See	See
25	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.117	0.0	NaN	0.019	0.0	1	1	NaN	0.917	0.005	0.000	880.366	883.638	See	See
30	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.003	0.0	NaN	0.005	0.0	-1	1	NaN	NaN	0.004	0.000	0.732	0.732	See	See
35	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.001	0.0	NaN	0.018	0.0	-1	5	NaN	1.000	0.000	0.000	7.622	10.458	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	120.957	0.000	NaN	0.000	0.121	-1	1	0.929	0.910	0.118	0.002	1026.007	1026.155	See	See
2	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.498	0.007	NaN	0.000	0.000	-1	1	0.929	1.000	0.000	0.000	1575.387	1747.066	See	See
3	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.575	0.059	NaN	0.000	2.575	-1	1	1.000	NaN	0.794	0.000	3.242	3.242	See	See
4	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.941	0.217	0.012	0.012	0.012	See	See
6	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	119.319	0.000	NaN	0.000	0.119	-1	5	0.946	NaN	0.799	0.000	149.294	149.294	See	See
7	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.952	0.019	NaN	0.000	0.001	-1	5	0.946	0.940	0.644	0.007	1.478	1.478	See	See
8	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.606	0.110	NaN	0.000	2.606	-1	5	1.000	1.000	0.001	0.000	3010.218	3241.338	See	See
9	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.004	0.001	NaN	0.000	0.004	-1	5	1.000	NaN	0.744	0.000	0.005	0.005	See	See
11	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	118.638	0.000	NaN	0.000	0.119	1	100	0.951	1.000	0.001	0.000	136793.341	147222.051	See	See
12	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	5.743	0.091	NaN	0.000	0.006	1	100	0.951	NaN	0.726	0.000	7.906	7.906	See	See
13	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.584	0.045	NaN	0.000	2.584	1	100	1.000	0.941	0.214	0.006	12.088	12.092	See	See
14	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	1.000	0.000	0.000	7.193	7.921	See	See
16	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	129.118	0.000	NaN	0.000	0.129	-1	100	0.951	0.910	0.117	0.002	1107.836	1107.962	See	See
17	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	3.246	0.052	NaN	0.000	0.003	-1	100	0.951	1.000	0.000	0.000	10490.704	11413.417	See	See
18	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.590	0.065	NaN	0.000	2.590	-1	100	1.000	NaN	0.001	0.000	2665.508	2665.508	See	See
19	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.006	0.001	NaN	0.000	0.006	-1	100	1.000	0.879	0.000	0.000	11.807	12.461	See	See
21	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	127.413	0.000	NaN	0.000	0.127	1	5	0.946	NaN	0.002	0.000	55931.340	55931.340	See	See
22	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	1.726	0.025	NaN	0.000	0.002	1	5	0.946	0.905	0.001	0.000	2228.687	2269.149	See	See
23	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.628	0.098	NaN	0.000	2.628	1	5	1.000	1.000	0.000	0.000	23216.849	29509.215	See	See
24	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.002	0.001	NaN	0.000	0.002	1	5	1.000	NaN	0.004	0.000	0.466	0.466	See	See
26	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	131.532	0.000	NaN	0.000	0.132	1	1	0.929	1.000	0.000	0.000	1073194.923	1343227.407	See	See
27	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.896	0.006	NaN	0.000	0.001	1	1	0.929	NaN	0.001	0.000	858.575	858.575	See	See
28	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.614	0.103	NaN	0.000	2.614	1	1	1.000	0.917	0.005	0.000	555.083	557.288	See	See
29	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	8.708	11.518	See	See
31	KNeighborsClassifier_kd_tree	True	predict	1000	1000	2	0.040	0.002	NaN	0.000	0.000	-1	1	0.891	0.905	0.001	0.000	54.586	56.096	See	See
32	KNeighborsClassifier_kd_tree	False	predict	1000	1000	2	0.021	0.001	NaN	0.001	0.000	-1	1	0.891	1.000	0.000	0.000	185.670	251.654	See	See
33	KNeighborsClassifier_kd_tree	True	predict	1000	1	2	0.005	0.000	NaN	0.000	0.005	-1	1	1.000	NaN	0.007	0.000	0.735	0.735	See	See
34	KNeighborsClassifier_kd_tree	False	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	1	1.000	0.879	0.000	0.000	4.991	5.209	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	False	fit	1000000	1000000	2	0.752	0.0	30	0.021	0.0	random	NaN	30	NaN	0.412	0.0	1.824	1.824	See	See
3	KMeans_tall	False	fit	1000000	1000000	2	0.584	0.0	30	0.027	0.0	k-means++	NaN	30	NaN	0.441	0.0	1.325	1.325	See	See
6	KMeans_tall	False	fit	1000000	1000000	100	6.104	0.0	30	0.131	0.0	random	NaN	30	NaN	2.821	0.0	2.164	2.164	See	See
9	KMeans_tall	False	fit	1000000	1000000	100	5.953	0.0	30	0.134	0.0	k-means++	NaN	30	NaN	2.826	0.0	2.107	2.107	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	0.011	0.000	random	0.000	30	0.001	0.0	0.0	9.214	10.968	See	See
2	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	random	1.000	30	1.000	0.0	0.0	9.951	12.076	See	See
4	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	0.011	0.000	k-means++	0.001	30	0.001	0.0	0.0	9.002	11.107	See	See
5	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.372	12.815	See	See
7	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	0.469	0.000	random	0.002	30	0.002	0.0	0.0	5.810	6.603	See	See
8	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	random	1.000	30	1.000	0.0	0.0	10.056	12.206	See	See
10	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	0.462	0.000	k-means++	0.002	30	0.002	0.0	0.0	5.364	5.947	See	See
11	KMeans_tall	False	predict	1000000	1	100	0.002	0.0	30	0.001	0.002	k-means++	1.000	30	1.000	0.0	0.0	11.477	14.226	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	False	fit	10000	10000	2	0.131	0.0	20	0.001	0.0	random	NaN	20	NaN	0.053	0.0	2.469	2.469	See	See
3	KMeans_short	False	fit	10000	10000	2	0.216	0.0	20	0.001	0.0	k-means++	NaN	20	NaN	0.080	0.0	2.697	2.697	See	See
6	KMeans_short	False	fit	10000	10000	100	0.205	0.0	20	0.039	0.0	random	NaN	20	NaN	0.113	0.0	1.811	1.811	See	See
9	KMeans_short	False	fit	10000	10000	100	0.562	0.0	20	0.014	0.0	k-means++	NaN	20	NaN	0.305	0.0	1.843	1.843	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	False	predict	10000	1000	2	0.002	0.000	20	0.009	0.000	random	0.002	20	0.001	0.001	0.0	2.550	3.124	See	See
2	KMeans_short	False	predict	10000	1	2	0.005	0.009	20	0.000	0.005	random	1.000	20	1.000	0.000	0.0	31.157	38.497	See	See
4	KMeans_short	False	predict	10000	1000	2	0.005	0.009	20	0.003	0.000	k-means++	0.002	20	0.001	0.001	0.0	9.781	9.945	See	See
5	KMeans_short	False	predict	10000	1	2	0.001	0.000	20	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.718	11.451	See	See
7	KMeans_short	False	predict	10000	1000	100	0.003	0.000	20	0.309	0.000	random	0.321	20	0.311	0.001	0.0	2.363	2.429	See	See
8	KMeans_short	False	predict	10000	1	100	0.002	0.000	20	0.001	0.002	random	1.000	20	1.000	0.000	0.0	7.920	8.963	See	See
10	KMeans_short	False	predict	10000	1000	100	0.003	0.000	20	0.309	0.000	k-means++	0.381	20	0.286	0.001	0.0	2.425	2.452	See	See
11	KMeans_short	False	predict	10000	1	100	0.001	0.000	20	0.001	0.001	k-means++	1.000	20	1.000	0.000	0.0	8.390	9.588	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	False	fit	1000000	1000000	100	12.061	0.0	[20]	0.066	0.000	NaN	NaN	NaN	NaN	NaN	1.995	0.0	6.045	6.045	See	See
3	LogisticRegression	False	fit	1000	1000	10000	4.104	0.0	[26]	0.019	0.004	NaN	NaN	NaN	NaN	NaN	0.937	0.0	4.382	4.382	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	False	predict	1000000	1000	100	0.000	0.0	[20]	2.635	0.0	NaN	NaN	NaN	NaN	0.56	0.000	0.0	0.834	0.958	See	See
2	LogisticRegression	False	predict	1000000	1	100	0.000	0.0	[20]	0.012	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.374	0.445	See	See
4	LogisticRegression	False	predict	1000	100	10000	0.002	0.0	[26]	4.690	0.0	NaN	NaN	NaN	NaN	0.35	0.003	0.0	0.540	0.545	See	See
5	LogisticRegression	False	predict	1000	1	10000	0.000	0.0	[26]	0.833	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.0	0.124	0.128	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	False	fit	1000	1000	10000	0.186	0.0	NaN	0.429	0.0	NaN	NaN	NaN	0.184	0.0	1.016	1.016	See	See
3	Ridge	False	fit	1000000	1000000	100	1.475	0.0	NaN	0.542	0.0	NaN	NaN	NaN	0.649	0.0	2.272	2.272	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	False	predict	1000	1000	10000	0.011	0.0	NaN	7.001	0.0	NaN	NaN	0.083	0.019	0.0	0.611	0.611	See	See
2	Ridge	False	predict	1000	1	10000	0.000	0.0	NaN	1.013	0.0	NaN	NaN	NaN	0.000	0.0	0.658	0.771	See	See
4	Ridge	False	predict	1000000	1000	100	0.000	0.0	NaN	4.782	0.0	NaN	NaN	1.000	0.000	0.0	0.566	0.671	See	See
5	Ridge	False	predict	1000000	1	100	0.000	0.0	NaN	0.013	0.0	NaN	NaN	NaN	0.000	0.0	0.672	0.829	See	See